从例子中学习的np困难问题

2008 Fifth International Conference on Fuzzy Systems and Knowledge Discovery Pub Date : 2008-10-18 DOI:10.1109/FSKD.2008.406

Bin Chen, Guangri Quan

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引用次数: 18

摘要

在设计实用的实例学习算法时，必须处理一些优化问题。主要的优化问题是:最小的特征子集选择、最小的决策树归纳和最小的k-DNF归纳。在本文中，我们证明了上述所有优化问题都是np困难的，并提出了新的贪心算法来解决这些问题。

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NP-Hard Problems of Learning from Examples

As designing practical algorithms of learning from examples, one has to deal with some optimization problems. The major optimization problems are: the smallest feature subset selection, the smallest decision tree induction, and the smallest k-DNF induction. In this paper, we show that all these optimization problems listed as above are NP-hard, and we present new greedy algorithms for solving these problems.

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来源期刊

2008 Fifth International Conference on Fuzzy Systems and Knowledge Discovery

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